SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Showing 201250 of 6748 papers

TitleStatusHype
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for MedicineCode1
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time SeriesCode1
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the EdgeCode1
WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series ClassificationCode1
StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time SeriesCode1
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological DataCode1
Ti-MAE: Self-Supervised Masked Time Series AutoencodersCode1
Diffusion-based Conditional ECG Generation with Structured State Space ModelsCode1
A Transformer-based Diffusion Probabilistic Model for Heart Rate and Blood Pressure Forecasting in Intensive Care UnitCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEGCode1
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological OrderCode1
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode DecompositionCode1
Deep Latent State Space Models for Time-Series GenerationCode1
Dynamic Sparse Network for Time Series Classification: Learning What to "see''Code1
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
Convolution-enhanced Evolving Attention NetworksCode1
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries ForecastingCode1
Integrating Multimodal Data for Joint Generative Modeling of Complex DynamicsCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal ModelingCode1
Multi-Dimensional Self Attention based Approach for Remaining Useful Life EstimationCode1
Sequential Predictive Conformal Inference for Time SeriesCode1
Phase2vec: Dynamical systems embedding with a physics-informed convolutional networkCode1
Copula Conformal Prediction for Multi-step Time Series ForecastingCode1
Axial-LOB: High-Frequency Trading with Axial AttentionCode1
Contrastive Domain Adaptation for Time-Series via Temporal MixupCode1
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time SeriesCode1
FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series ForecastingCode1
Diffusion Generative Models in Infinite DimensionsCode1
Understanding Cryptocoins Trends CorrelationsCode1
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural NetworksCode1
Federated Learning for 5G Base Station Traffic ForecastingCode1
Spatio-Temporal Meta-Graph Learning for Traffic ForecastingCode1
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive ModelsCode1
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box AttackCode1
Finding active galactic nuclei through FinkCode1
Are we certain it's anomalous?Code1
SETAR-Tree: A Novel and Accurate Tree Algorithm for Global Time Series ForecastingCode1
Backdoor Attacks on Time Series: A Generative ApproachCode1
Advancing the State-of-the-Art for ECG Analysis through Structured State Space ModelsCode1
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural NetworksCode1
WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley ValuesCode1
Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement PredictionCode1
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
Automatic Change-Point Detection in Time Series via Deep LearningCode1
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural NetworksCode1
Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecastingCode1
Probabilistic Decomposition Transformer for Time Series ForecastingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified